Multi-view Locality Low-rank Embedding for Dimension Reduction

摘要

During the last decades, we have witnessed a surge of interest in learning a low-dimensional space with discriminative information from one single view. Even though most of them can achieve satisfactory performance in certain situations, they fail to fully consider the information from multiple views which are highly relevant but sometimes look different from each other. Besides, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view subspace learning methods. Therefore, how to learn an appropriate subspace which could maintain valuable information from multi-view features is of vital importance but challenging. To tackle this problem, this paper proposes a novel multi-view dimension reduction method named Multi-view Locality Low-rank Embedding for Dimension Reduction (MvL(2)E). MvL(2)E mainly focuses on capturing a common low-dimensional embedding among multiple different views, which makes full use of correlations between multi-view features by adopting low-rank representations. Meanwhile, it aims to maintain the correlations and construct a suitable manifold structure to capture: the low-dimensional embedding for multi-view features. A centroid based scheme is designed to' get one common low-dimensional manifold space and force multiple views to learn from each other. And an iterative alternating strategy is developed to obtain the optimal solution of MvL(2)E. The proposed method is evaluated on 5 benchmark datasets. Comprehensive experiments show that our proposed MvL(2)E can achieve comparable performance with previous approaches proposed in recent works of literature. (C) 2019 Elsevier B.V. All rights reserved.